The Data First Approach to Supply Chain Transformation

There is no doubt that supply chain success is synonymous with operational excellence. The more efficient, agile, and responsive a supply chain is the better it performs and the more satisfied customers are. However, the key to an agile supply chain is to be data-driven, and for this, good data is necessary. Good data provides better visibility which in turn provides better insights for better decisions.

In today’s digital world, the availability of data seems to be abundant, yet for supply chains this is not the case. Companies struggle for access to trade data and question the quality of their existing data. On average, only 44% of organizations trust their data and more than 30% of the data they have is perceived to be inaccurate. Without reliable data, effective decision-making and risk management is difficult. The two most common problems companies face when in regards to data are access and quality.

The Data Challenge

Supply chain and logistics people spend thousands of hours mining for data - calling different trade partners, emailing, and searching for data on their shipments. With the amount of data available in today’s digitized world, accessibility to data should not be a problem, yet most of the critical trade data needed to operate supply chains efficiently is often hidden in siloed systems or simply not made available by third-party providers who own and hold that information close to the vest. Without a centralized database insights are difficult to gather and hidden values within the supply chain cannot be uncovered.

When it comes to data quality, most of the data pulled from the vast network of disparate supply chain systems is riddled with holes, duplicates, errors, out-of-date information, and latencies that lead to complications and process disruptions when leveraged by existing visibility solutions. To ensure its integrity the data must go through an enrichment process. Cleansing, standardization, normalization, harmonization, and mapping is key to generating trustworthy insights.

Technology Disrupting Supply Chain

Today when supply chain and logistics teams are tasked with building efficient systems that cater to the needs of the organization and its customers, technology plays a key role. Technologies such as the Internet of Things (IoT), Artificial Intelligence, and Machine Learning are transforming supply chains around the world.

Internet of Things (IoT)

Today, thanks to IoT, the variety and availability of data is beyond number, providing massive amounts of data for visibility software to leverage and unlock opportunities within supply chains. Due to the enormous amount of data that IoT generates, traditional processing powers are not enough - only Artificial Intelligence and Machine Learning have the capability to leverage it.

Artificial Intelligence (AI) & Machine Learning

AI is the ability to train machines to mimic the behaviors of humans, while machine learning actually does the “learning”. Unlike other systems, machine learning has the capability to automatically learn and improve from experience. It works by recognizing patterns and making decisions the way we do, but at speeds no human is capable of.

The most basic of AI and machine learning applications can automate the routine and mechanical tasks that we do, so we can be free to focus on higher value tasks. But, it’s true value is in identifying insights and offering guidance based on learned experiences and historical data. The more data a machine learning algorithm is able to ingest, the smarter it becomes and the better it can distill and map information from vast data pools when visualizations become too complex and writing rules becomes impossible because of unstructured data and permutations within available data.

Advantages of Supply Chain Transformation

With the right data, businesses can unleash tremendous potential not only for their supply chain and logistics, but other functions including everything from sales and marketing to production. Quality data results in a clear view of the supply chain allowing for strategic planning and execution, but insights based on unreliable data points and metrics are meaningless. To address this challenge, supply chain leaders need to adopt a data-first approach and spend time understanding their problems before leveraging technologies to help them.